Daniel Chalev

Stop Using RAG as Memory — Daniel Chalef, Zep

Jul 22, 2025 · 7:02

Daniel Chalef, founder of Zep, explains why using RAG as memory for AI agents fails: vector databases rely on semantic similarity, not business relevance, so irrelevant facts pollute retrieval and cause hallucinations. He advocates for domain-aware memory using temporal knowledge graphs, demonstrated through Zep's open-source Graphiti framework. Chalef shows how developers define custom entity types (e.g., financial goals, debts) with Pydantic or Zod schemas, register them as ontology objects, and build tools that concurrently search Zep filtered by node type. A live demo of a finance coach agent illustrates Zep capturing structured facts like rent payments into a knowledge graph, enabling precise, context-rich memory for accurate agent reasoning.

Memory Masterclass: Make Your AI Agents Remember What They Do! — Mark Bain, AIUS

Jun 27, 2025 · 51:25

Mark Bain, Vasilia Markovits, Alex Gilmore, and Daniel Chalev demonstrate that AI memory requires causal relationships and graph databases, not just vector similarity, to solve hallucinations and enable agentic workflows. Bain argues that memory is any data affecting change, and that attention, diffusion, and VAEs follow the same geometric principles as gravity and entropy. Alex shows Neo4j's MCP server storing semantic memory as entities and relationships retrieved across conversations. Vasilia demos Cognee building semantic graphs from GitHub data for agentic hiring decisions. Daniel presents Graphiti's domain-aware memory using custom Pydantic schemas to filter irrelevant facts. Bain introduces a GraphRAG chat arena that switches between memory solutions on a single Neo4j graph, testing different implementations for episodic and temporal recall.